Abstract

Background: Classification of newly resolved protein structures is important in understanding their architectural, evolutionary and functional relatedness to known protein structures. Among various efforts to improve the database of Structural Classification of Proteins (SCOP), automation has received particular attention. Herein, we predict the deepest SCOP structural level that an unclassified protein shares with classified proteins with an equal number of secondary structure elements (SSEs).

Results: We compute a coefficient of dissimilarity (omega) between proteins, based on structural and sequence-based descriptors characterising the respective constituent SSEs. For a set of 1,661 pairs of proteins with sequence identity up to 35%, the performance of omega in predicting shared Class, Fold and Super-family levels is comparable to that of DaliLite Z score and shows a greater than four-fold increase in the true positive rate (TPR) for proteins sharing the Family level. On a larger set of 600 domains representing 200 families, the performance of Z score improves in predicting a shared Family, but still only achieves about half of the TPR of omega. The TPR for structures sharing a Superfamily is lower than in the first dataset, but omega performs slightly better than Z score. Overall, the sensitivity of omega in predicting common Fold level is higher than that of the DaliLite Z score.

Conclusion: Classification to a deeper level in the hierarchy is specific and difficult. So the efficiency of omega may be attractive to the curators and the end-users of SCOP. We suggest omega may be a better measure for structure classification than the DaliLite Z score, with the caveat that currently we are restricted to comparing structures with equal number of SSEs.

Item Type:

Article

Schools/Departments:

University of Nottingham UK Campus > Faculty of Science > School of Chemistry